Bibliographic record
Abstract
Porn production, like all forms of labour, entails certain occupational health and safety (OHS) risks. Porn production has generally not been subject to state occupational health oversight, and porn workers have instead implemented self-regulatory OHS systems. However, in California, where the industry is most established, governmental and non-governmental bodies have made several paternalist attempts to legislate standardised OHS protocols. Their proposed legislation exceptionalises sex work as uniquely dangerous while failing to tailor guidance to the specific needs of and practices associated with porn work. This is largely because: 1) regulators are ignorant of porn’s self-regulatory processes; 2) industry self-regulation conceptualises the occupational hazard on porn sets as infectious bodily fluids, whereas external regulators perceive the hazard as sex itself; and 3) regulators devalue porn work and so do not take the viability of the labour into account when evaluating protocol effectiveness. Using critical-interpretive medical anthropology involving fieldwork and interviews with porn workers and a critical analysis of porn OHS texts, I argue that porn health protocols should be left to industry self-determination, to be developed by porn workers rather than for them.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.124 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.084 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".